



"""simple docstring"""
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import RegNetForImageClassification, RegNetModel
					from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class       snake_case_    :






							def __init__( self      ,  a_      ,  a_=3      ,  a_=3_2      ,  a_=3      ,  a_=1_0      ,  a_=[1_0, 2_0, 3_0, 4_0]      ,  a_=[1, 1, 2, 1]      ,  a_=True      ,  a_=True      ,  a_="relu"      ,  a_=3      ,  a_=None      ,  ):
									a_  :   int			    =  parent
									a_  :   Union[str, Any]			    =  batch_size
									a_  :   Dict			    =  image_size
									a_  :   Optional[Any]			    =  num_channels
									a_  :   int			    =  embeddings_size
									a_  :   Dict			    =  hidden_sizes
									a_  :   List[Any]			    =  depths
									a_  :   int			    =  is_training
									a_  :   Dict			    =  use_labels
									a_  :   Tuple			    =  hidden_act
									a_  :   str			    =  num_labels
									a_  :   int			    =  scope
									a_  :   Union[str, Any]			    =  len(SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self   ):
									a_  :   Tuple			    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

									a_  :   int			    =  None
									if self.use_labels:
											a_  :   Dict			    =  ids_tensor([self.batch_size]      ,  self.num_labels   )

									a_  :   Dict			    =  self.get_config()

									return config, pixel_values, labels






							def snake_case_	( self   ):
									return RegNetConfig(
									    num_channels=self.num_channels      ,  embeddings_size=self.embeddings_size      ,  hidden_sizes=self.hidden_sizes      ,  depths=self.depths      ,  hidden_act=self.hidden_act      ,  num_labels=self.num_labels      ,  )






							def snake_case_	( self      ,  a_      ,  a_      ,  a_   ):
									a_  :   List[Any]			    =  RegNetModel(config=SCREAMING_SNAKE_CASE_   )
									model.to(SCREAMING_SNAKE_CASE_   )
									model.eval()
									a_  :   Dict			    =  model(SCREAMING_SNAKE_CASE_   )
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape      ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)      ,  )






							def snake_case_	( self      ,  a_      ,  a_      ,  a_   ):
									a_  :   Any			    =  self.num_labels
									a_  :   Optional[int]			    =  RegNetForImageClassification(SCREAMING_SNAKE_CASE_   )
									model.to(SCREAMING_SNAKE_CASE_   )
									model.eval()
									a_  :   Any			    =  model(SCREAMING_SNAKE_CASE_      ,  labels=SCREAMING_SNAKE_CASE_   )
									self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)   )






							def snake_case_	( self   ):
									a_  :   Optional[Any]			    =  self.prepare_config_and_inputs()
									a_  :   List[str]			    =  config_and_inputs
									a_  :   Optional[Any]			    =  {"""pixel_values""": pixel_values}
									return config, inputs_dict




@require_torch
class       snake_case_    (    _lowerCAmelCase  ,_lowerCAmelCase  ,unittest.TestCase					):
							__lowerCAmelCase            =       (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
							__lowerCAmelCase            =       (
							    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowerCAmelCase            =       False
							__lowerCAmelCase            =       False
							__lowerCAmelCase            =       False
							__lowerCAmelCase            =       False






							def snake_case_	( self   ):
									a_  :   Optional[int]			    =  RegNetModelTester(self   )
									a_  :   Union[str, Any]			    =  ConfigTester(self      ,  config_class=SCREAMING_SNAKE_CASE_      ,  has_text_modality=SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self   ):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()






							def snake_case_	( self   ):
									return






							@unittest.skip(reason="RegNet does not use inputs_embeds"   )
							def snake_case_	( self   ):
									pass






							@unittest.skip(reason="RegNet does not support input and output embeddings"   )
							def snake_case_	( self   ):
									pass






							def snake_case_	( self   ):
									a_  :   int			    =  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_  :   List[str]			    =  model_class(SCREAMING_SNAKE_CASE_   )
											a_  :   str			    =  inspect.signature(model.forward   )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a_  :   Tuple			    =  [*signature.parameters.keys()]

											a_  :   Dict			    =  ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]      ,  SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self   ):
									a_  :   Dict			    =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self   ):
									a_  :   List[Any]			    =  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_  :   int			    =  model_class(config=SCREAMING_SNAKE_CASE_   )
											for name, module in model.named_modules():
													if isinstance(SCREAMING_SNAKE_CASE_      ,  (nn.BatchNormad, nn.GroupNorm)   ):
															self.assertTrue(
															    torch.all(module.weight == 1   )      ,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,  )
															self.assertTrue(
															    torch.all(module.bias == 0   )      ,  msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""      ,  )






							def snake_case_	( self   ):
									def check_hidden_states_output(a_      ,  a_      ,  a_   ):
											a_  :   Any			    =  model_class(SCREAMING_SNAKE_CASE_   )
											model.to(SCREAMING_SNAKE_CASE_   )
											model.eval()

											with torch.no_grad():
													a_  :   Any			    =  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_   )   )

											a_  :   Union[str, Any]			    =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											a_  :   Any			    =  self.model_tester.num_stages
											self.assertEqual(len(SCREAMING_SNAKE_CASE_   )      ,  expected_num_stages + 1   )

											# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]   )      ,  [self.model_tester.image_size // 2, self.model_tester.image_size // 2]      ,  )

									a_  :   Dict			    =  self.model_tester.prepare_config_and_inputs_for_common()
									a_  :   Optional[int]			    =  ["""basic""", """bottleneck"""]
									for model_class in self.all_model_classes:
											for layer_type in layers_type:
													a_  :   Optional[int]			    =  layer_type
													a_  :   Dict			    =  True
													check_hidden_states_output(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_   )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													a_  :   Tuple			    =  True

													check_hidden_states_output(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self   ):
									a_  :   List[str]			    =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_   )






							@slow
							def snake_case_	( self   ):
									for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_  :   List[Any]			    =  RegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_   )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_   )



def 							lowerCAmelCase_    (				)      ->   Tuple:
		a_  :   Any			    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
		return image




@require_torch
@require_vision
class       snake_case_    (    unittest.TestCase					):






							@cached_property
							def snake_case_	( self   ):
									return (
									    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
									    if is_vision_available()
									    else None
									)






							@slow
							def snake_case_	( self   ):
									a_  :   Any			    =  RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(SCREAMING_SNAKE_CASE_   )

									a_  :   List[str]			    =  self.default_image_processor
									a_  :   Any			    =  prepare_img()
									a_  :   Union[str, Any]			    =  image_processor(images=SCREAMING_SNAKE_CASE_      ,  return_tensors="pt"   ).to(SCREAMING_SNAKE_CASE_   )

									# forward pass
									with torch.no_grad():
											a_  :   List[Any]			    =  model(**SCREAMING_SNAKE_CASE_   )

									# verify the logits
									a_  :   Dict			    =  torch.Size((1, 1_0_0_0)   )
									self.assertEqual(outputs.logits.shape      ,  SCREAMING_SNAKE_CASE_   )

									a_  :   Dict			    =  torch.tensor([-0.4_180, -1.5_051, -3.4_836]   ).to(SCREAMING_SNAKE_CASE_   )

									self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  SCREAMING_SNAKE_CASE_      ,  atol=1e-4   )   )




import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






